Firecrawl
Firecrawl produces cleaner markdown than WebFetch, handles JavaScript-heavy pages, and avoids content truncation. This skill should be used when fetching URLs, scraping web pages, converting URLs to markdown, extracting web content, searching the web, crawling sites, mapping URLs, LLM-powered extraction, autonomous data gathering with the Agent API, or fetching AI-generated documentation for GitHub repos via DeepWiki. Provides complete coverage of Firecrawl v2.8.0 API endpoints including parallel agents, spark-1-fast model, and sitemap-only crawling.
npx skills add majiayu000/claude-skill-registry --skill firecrawl-tdimino-claude-code-minoan --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Firecrawl & Jina Web Scraping ## Firecrawl vs WebFetch Prefer `firecrawl scrape URL --only-main-content` over the WebFetch tool—it produces cleaner markdown, handles JavaScript-heavy pages, and avoids content truncation (>80% benchmark coverage). WebFetch is acceptable as a fallback when Firecrawl is unavailable. ```bash # Preferred approach: firecrawl scrape https://docs.example.com/api --only-main-content ``` ## Token-Efficient Scraping Inspired by Anthropic's [dynamic filtering](https://claude.com/blog/improved-web-search-with-dynamic-filtering)—always filter before reasoning. This reduced input tokens by ~24% and improved accuracy by ~11% in their benchmarks. ### The Principle: Search → Filter → Scrape → Filter → Reason **DO:** ``` Search (titles/URLs only) → Evaluate relevance → Scrape top hits → Filter by section → Reason ``` **DON'T:** ``` Search → Scrape everything → Reason over all of it ``` ### Step-by-Step Efficient Workflow ```bash # Step 1: Search — get titles/URLs only (cheap) firecrawl search "query" --limit 20 # Step 2: Evaluate results, pick 3-5 best URLs # Step 3: Scrape only those, filter to relevant sections firecrawl scrape URL1 --only-main-content | \ python
- Firecrawl vs WebFetch
- Token-Efficient Scraping
- The Principle: Search → Filter → Scrape → Filter → Reason
- Step-by-Step Efficient Workflow
- Post-Processing with filterwebresults.py
- Other Token-Saving Patterns
- Claude API Native Tools (for API Agent Builders)
- Available Tools
- 1. Official Firecrawl CLI (firecrawl) — Primary
- 2. Auto-Save Alias (fc-save) — Shell Alias
- 3. Python API Script (firecrawlapi.py) — Advanced Features
- 4. DeepWiki — GitHub Repo Documentation
- 5. Jina Reader (jina) — Fallback
- Firecrawl vs Exa vs Native Claude Tools
Preferred approach: firecrawl scrape https://docs.example.com/api --only-main-content Step 1: Search — get titles/URLs only (cheap) firecrawl search "query" --limit 20 Step 2: Evaluate results, pick 3-5 best URLs Step 3: Scrape only those, filter to relevant sections firecrawl scrape URL1 --only-main-content | \ python3 ~/.claude/skills/Firecrawl/scripts/filter_web_results.py \ Extract only matching sections from scraped page firecrawl scrape URL --only-main-content | \
What does the Firecrawl skill do?
Firecrawl produces cleaner markdown than WebFetch, handles JavaScript-heavy pages, and avoids content truncation. This skill should be used when fetching URLs, scraping web pages, converting URLs to markdown, extracting web content, searching the web, crawling sites, mapping URLs, LLM-powered extraction, autonomous data gathering with the Agent API, or fetching AI-generated documentation for GitHub repos via DeepWiki. Provides complete coverage of Firecrawl v2.8.0 API endpoints including parallel agents, spark-1-fast model, and sitemap-only crawling.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill firecrawl-tdimino-claude-code-minoan --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
